Specific and Long-Term Effects of Nova Scotia's Graduated Licensing Program
Bibliographic record
Abstract
A graduated licensing (GL) program was introduced in Nova Scotia, Canada, in October 1994. Previous research has shown that it reduced collisions in the short term. The present study examined the relative contribution of each stage of the program (i.e., learner and intermediate levels) and the program's impact after beginning drivers graduated to full licensure. The research focused on teenage beginning drivers (age 16-17), but the effects on older beginners also was examined. Per-driver crash rates of two groups of novices selected from driver records in Nova Scotia were compared. One group (pre-GL) received their learner's permits during the 2 years before the program was implemented, and the second group (GL) received their learner's permits during the 2 years after implementation. The findings clearly establish that most of the collision reduction in Nova Scotia's program occurred during the first year of the program, particularly during the first 6 months when the majority of novices were driving under supervision. The collision rate for 16 to 17-year-old GL novices was 50% lower than the rate for pre-GL novices during the 6 months after they received their learner's permits, and about 10% lower during their first 2 years of licensure when unsupervised driving from midnight to 5 A.M. was prohibited. Much of this improvement for 16 to 17-year-olds occurred during restricted night hours. Collision rates also were lower during nonrestricted hours in the initial 6 months of licensure. The 3-month "time discount" for driver education provided no safety benefit, and GL novices with driver education had collision rates that were not lower than pre-GL novices. There was no long-term effect found for the program after 16 to 17-year-olds graduated to full licensure. For older beginning drivers, crash rates during the first year after obtaining a learner's permit showed a 31% reduction. This effect diminished rapidly. There was only a 2% reduction during the first year of licensure, and crash rates increased during the following 2 years. Overall the data indicate substantial benefits of graduated licensing for 16 to 17-year-old beginners, but no benefits beyond the learner stage for older beginners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".